BACKGROUND: Functional imaging such as CT perfusion can detect morphological and hemodynamic changes in hepatocellular carcinoma (HCC). Pre-carcinoma and early HCC nodules are difficult to differentiate by observing only their hemodynamics changes. The present study aimed to investigate hemodynamic parameters and evaluate their differential diagnostic cut-off between pre-carcinoma and early HCC nodules using CT perfusion and receiver operating characteristic (ROC) curves. METHODS: Male Wistar rats were randomly divided into control (n=20) and experimental (n=70) groups. Diethylnitrosamine (DEN) was used to induce pre-carcinoma and early HCC nodules in the experimental group. Perfusion scanning was carried out on all survival rats discontinuously from 8 to 16 weeks. Hepatic portal perfusion (HPP), hepatic arterial fraction (HAF), hepatic arterial perfusion (HAP), hepatic blood volume (HBV), hepatic blood flow (HBF), mean transit time (MTT) and permeability of capillary vessel surface (PS) data were provided by mathematical deconvolution model. The perfusion parameters were compared among the three groups of rats (control, pre-carcinoma and early HCC groups) using the Kruskal-Wallis test and analyzed with ROC curves. Histological examination of the liver tissues with hematoxylin and eosin staining was performed after CT scan. RESULTS: For HPP, HAF, HBV, HBF and MTT, there were significant differences among the three groups (P<0.05). HAF had the highest areas under the ROC curves: 0.80 (control vs pre-carcinoma groups) and 0.95 (control vs early HCC groups) with corresponding optimal cut-offs of 0.37 and 0.42, respectively. The areas under the ROC curves for HPP was 0.79 (control vs pre-carcinoma groups) and 0.92 (control vs early HCC groups) with corresponding optimal cut-offs of 136.60 mL/min/100 mg and 108.47 mL/min/100 mg, respectively. CONCLUSIONS: CT perfusion combined with ROC curve analysis is a new diagnosis model for distinguishing between pre-carcinoma and early HCC nodules. HAF and HPP are the ideal reference indices.
BACKGROUND: Functional imaging such as CT perfusion can detect morphological and hemodynamic changes in hepatocellular carcinoma (HCC). Pre-carcinoma and early HCC nodules are difficult to differentiate by observing only their hemodynamics changes. The present study aimed to investigate hemodynamic parameters and evaluate their differential diagnostic cut-off between pre-carcinoma and early HCC nodules using CT perfusion and receiver operating characteristic (ROC) curves. METHODS: Male Wistar rats were randomly divided into control (n=20) and experimental (n=70) groups. Diethylnitrosamine (DEN) was used to induce pre-carcinoma and early HCC nodules in the experimental group. Perfusion scanning was carried out on all survival rats discontinuously from 8 to 16 weeks. Hepatic portal perfusion (HPP), hepatic arterial fraction (HAF), hepatic arterial perfusion (HAP), hepatic blood volume (HBV), hepatic blood flow (HBF), mean transit time (MTT) and permeability of capillary vessel surface (PS) data were provided by mathematical deconvolution model. The perfusion parameters were compared among the three groups of rats (control, pre-carcinoma and early HCC groups) using the Kruskal-Wallis test and analyzed with ROC curves. Histological examination of the liver tissues with hematoxylin and eosin staining was performed after CT scan. RESULTS: For HPP, HAF, HBV, HBF and MTT, there were significant differences among the three groups (P<0.05). HAF had the highest areas under the ROC curves: 0.80 (control vs pre-carcinoma groups) and 0.95 (control vs early HCC groups) with corresponding optimal cut-offs of 0.37 and 0.42, respectively. The areas under the ROC curves for HPP was 0.79 (control vs pre-carcinoma groups) and 0.92 (control vs early HCC groups) with corresponding optimal cut-offs of 136.60 mL/min/100 mg and 108.47 mL/min/100 mg, respectively. CONCLUSIONS: CT perfusion combined with ROC curve analysis is a new diagnosis model for distinguishing between pre-carcinoma and early HCC nodules. HAF and HPP are the ideal reference indices.
Authors: Marcel C Langenbach; Thomas J Vogl; Isabelle von den Driesch; Benjamin Kaltenbach; Jan-Erik Scholtz; Renate M Hammerstingl; Tatjana Gruber-Rouh Journal: Eur Radiol Date: 2019-06-24 Impact factor: 5.315